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Record W2034529304 · doi:10.1109/tia.2014.2387477

Influences of Power Electronic Converters on Voltage–Current Behaviors During Faults in DGUs—Part I: Wind Energy Conversion Systems

2015· article· en· W2034529304 on OpenAlexaff
S. A. Saleh, A. S. Aljankawey, Muhammad Abu‐Khaizaran, Basim Alsayid

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2015
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsConvertersVoltageWind powerGridFault (geology)InterconnectionGenerator (circuit theory)Power (physics)Computer scienceElectrical engineeringAC powerCurrent (fluid)Control theory (sociology)EngineeringPhysicsTelecommunicationsControl (management)Mathematics

Abstract

fetched live from OpenAlex

The growing interest in clean and sustainable electric energy pushes toward increasing the interconnection of wind energy conversion systems (WECSs) to utility grids. The designs of the majority of WECSs employ power electronic converters (PECs), which generally have nonlinear and switched characteristics. The characteristics, operation, and control of PECs in WECSs can result in nonconventional voltage-current behaviors during faults. This paper investigates the voltage-current behaviors during faults that occur in grid-connected WECSs. Two types of WECSs are considered in this paper, which are the doubly fed induction generators and permanent magnet generator based WECSs. The voltage-current behaviors are investigated in experimentations for different faults occurring in different parts of the test grid-connected WECSs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.239
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations41
Published2015
Admission routes1
Has abstractyes

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